The Employability of Engineers in the Era of Industry 4.0 ^†

Abstract

This study explores the employability of engineers in the context of Industry 4.0, focusing on the KASH model (Knowledge, Attitude, Skills, Habits). Using a sequential exploratory design, we collected data from 800 engineers in Morocco through an online questionnaire. The findings reveal that attitude and knowledge have a significant impact on employability, whereas skills and habits show moderate to weak effects. The study employs Partial L6east Squares (PLS) Structural Equation Modeling to validate the model, demonstrating a strong predictive relevance (Q² = 0.714) and a high coefficient of determination (R² = 0.716). The results suggest that engineering schools should emphasize attitude and knowledge development to enhance employability in the rapidly evolving job market of Industry 4.0.

1. Introduction

Employability in higher education is a multidimensional concept that goes beyond the mere acquisition of a job after graduation. It encompasses a combination of knowledge, skills, attitudes, and personal attributes that enable graduates to navigate the labor market, adapt to change, and contribute effectively to the workplace [1].

Industry 4.0, characterized by the integration of advanced technologies such as artificial intelligence, the Internet of Things (IoT), and automation, is profoundly transforming the job market [2,3,4]. Engineers, at the heart of this revolution, are facing major transformations in their professions, as traditional roles evolve to incorporate cyber-physical systems and data-driven decision-making [2,3,4]. This industrial revolution is not limited to an increased demand for specialized technical skills but also requires essential behavioral skills. Communication, problem-solving, adaptability, and collaboration skills are becoming crucial for navigating increasingly complex and interconnected work environments [5]. This context raises a central question: How are the skills required for engineers evolving, and what solutions can enhance their employability?

To assess the relationship between these dimensions and engineers’ employability, we conducted a quantitative study involving 800 Moroccan engineers across various industrial sectors. Data were collected through an online questionnaire structured around the four dimensions of the KASH model [6,7], and analyzed using Structural Equation Modeling (SEM) with SMART PLS. This approach enabled the evaluation of each dimension’s contribution to engineers’ employability. The KASH model (Knowledge, Attitude, Skills, and Habits) offers a comprehensive framework for this analysis [6], suggesting that, beyond technical knowledge, an individual’s attitude, behavioral skills, and work habits are key drivers of professional success [7].

1.1. Literature Review

Recent studies highlight the crucial role of digitalization in reshaping industrial engineering education to meet labor market demands. A survey among 155 industrial engineering students was conducted, revealing that most of them recognize the significance of digitalization but feel that their university curricula lack sufficient exposure to emerging technologies. The study emphasizes the need for integrating courses on artificial intelligence, big data, and smart manufacturing to bridge the skills gap between academia and industry. Moreover, it underscores the importance of counseling students to overcome psychological barriers related to the digital transition, such as resistance to change and fear of automation [8].

1.1.1. Industrial Employability Model for Engineers

Employability is defined as a set of achievements, skills, understandings, and personal attributes that make graduates more likely to gain employment and be successful in their chosen occupations, which benefits themselves, the workforce, the community, and the economy [8,9]. In the engineering context, employability extends to the ability to apply technical knowledge, problem-solving skills, teamwork, and adaptability in professional environments while meeting the dynamic demands of the industry [9,10]. Recent studies emphasize the importance of integrating industry-relevant competencies, such as digital literacy and interdisciplinary collaboration, into engineering education to enhance employability in the rapidly evolving industrial landscape [11,12]. Furthermore, the role of soft skills, such as communication and emotional intelligence, has been increasingly recognized as critical for engineering graduates to thrive in diverse and globalized work environments [13,14,15].

1.1.2. Global and Moroccan Trends in Employability Factors

Moroccan engineers’ employability factors exhibit both unique challenges and commonalities when compared to global trends in regions such as Europe, the United States, and Asia. Below, we listed significant insights:

Skills	Morocco	Europe	U. S	Asia
Technical skills	Personal Management Skills (PMS), Teamwork Skills (TWS), and Work Safety (WS) are highly demanded in the professional context. However, fundamental technical skills were perceived to have a smaller impact on employability [9].	Places a high emphasis on specialization and practical training aligned with industry needs. Digital transformation skills are increasingly integrated into curricula [10].	The National Science Foundation highlights the growing importance of multidisciplinary expertise and innovation in the STEM workforce. It emphasizes the role of emerging technologies like AI and data science in driving innovation and economic growth [11].	Engineers with strong technical skills were more likely to secure employment, with higher employment rates and a greater likelihood of holding permanent positions. [12].
Soft skills	Moroccan employers highly value communication, organization, adaptability, and tech proficiency. They also seek responsibility, autonomy, teamwork, and problem-solving skills. Project management, writing, and leadership are moderately required. Stress management, ethics, and entrepreneurship are less emphasized [13].	Soft skills are considered equally important for thriving in dynamic work environments and include leadership, entrepreneurship, creativity, and empathy. Engineers are seen as communicators and facilitators. Interdisciplinary competencies are identified as key competencies in the face of a changing environment [14].	The Accreditation Board for Engineering and Technology (ABET) requires students to: Communicate effectively with diverse audiences, recognize and uphold ethical responsibilities, and work effectively within a team [15].	Engineers with strong problem-solving and critical thinking skills can approach complex issues methodically and creatively. These skills are highly valued by employers [16].

1.1.3. The Case Study

This study explores how engineering education can be adapted to meet the demands of Industry 4.0. It focuses on a group of 24 senior engineering students who participated in a specialized course designed to develop both technical and soft skills needed for the digital and interconnected workplace. The study emphasizes active learning through real-world projects, team collaboration, and exposure to technologies such as automation, robotics, and data analytics. Its main goal is to foster a mindset of adaptability, innovation, and lifelong learning. The case offers a practical model for integrating future-ready competencies into engineering curricula [16].

1.1.4. The 4IR Generic Skills (GS4IR)

The 4th Industrial Revolution (4IR) Generic Skills (GS4IR) are a set of transferable, essential skills and attributes that equip graduates and workers to meet the evolving demands of technologically advanced workplaces. These skills are critical across disciplines and industries, especially in engineering fields, to adapt to rapid technological changes such as AI, robotics, and biotechnology.

Various models outline the skills needed for the 4IR:

• The McKinsey Global Institute identifies 25 skills across five categories: physical, basic cognitive, higher cognitive, emotional, and technological skills [17].
• The OECD emphasizes three main skills: self-management (managing the present), social intelligence (connecting with others), and innovation (creating change), highlighting social and emotional skills as vital for future success [18].
• The World Economic Forum’s Future of Jobs report lists ten key skills for 4IR, including complex problem solving, critical thinking, creativity, emotional intelligence, and cognitive flexibility [19].

1.1.5. The Moroccan Context: Challenges and Opportunities

Morocco is actively modernizing its industry through initiatives like the Industrial Acceleration Plan (PAI) 2014–2020 [20], positioning engineers as key drivers of innovation in sectors such as automotive, aerospace, renewable energy, and digital transformation [21]. In the automotive sector, engineers contribute to manufacturing efficiency, R&D, and tech integration, helping Morocco emerge as a major African hub [22]. In aerospace, they optimize supply chains and foster innovation, supporting the sector’s growth as a global outsourcing destination [23]. In renewable energy, engineers design and implement large-scale solar and wind projects, advancing the country’s shift toward sustainability [24]. In digital transformation, they help overcome technological barriers and boost the competitiveness of Moroccan firms [25]. Yet, significant challenges persist: Technological obsolescence: Many graduates lack exposure to tools like AI or IoT systems [26]. Global competition: Multinationals seek hybrid-skilled engineers, disadvantaging local talent. Demographic pressure: With 60% of the population under 35, demand for engineering jobs far exceeds supply [27]. These challenges highlight the urgent need to reshape engineering education to improve employability in a globalized economy.

1.1.6. Engineering Education Reform

To effectively reform engineering education in alignment with Industry 4.0, universities should implement several key curriculum changes and pedagogical strategies:

• Integration of AI and Data Literacy Courses

Universities must incorporate courses on artificial intelligence (AI), machine learning, data analytics, big data, cloud computing, and automation into engineering curricula. These subjects are critical for equipping students with the technical skills required to thrive in the digital transformation of industry [28].

• Development of Soft Skills through Experiential Learning

Soft skills such as leadership, communication, teamwork, and adaptability are increasingly valued alongside technical expertise. Baahmad (2025) demonstrated that applying Kolb’s Experiential Learning Theory—focusing on active experimentation and reflective observation—can effectively promote these skills [28].

• Adoption of Interdisciplinary Learning Tracks

Engineering education should broaden beyond traditional technical boundaries by integrating interdisciplinary tracks that combine engineering with business, environmental science, design, humanities, and social sciences. This approach enhances students’ problem-solving abilities, critical thinking, creativity, and adaptability by exposing them to diverse perspectives and complex real-world challenges [29,30].

2. Materials and Methods

2.1. Research Design and Methodological Approach

This study adopts a quantitative approach within a sequential exploratory design, using the KASH model (Knowledge, Attitude, Skills, and Habits) as the theoretical framework. Data were collected through a structured online questionnaire, developed from an extensive literature review and validated by expert input. The questionnaire comprises closed-ended Likert-scale items and categorical variables, structured around the four dimensions of the KASH model. It was disseminated online via professional networks, engineering alumni platforms, and email invitations.

To ensure statistical robustness, Partial Least Squares Structural Equation Modeling (PLS-SEM) was conducted using SmartPLS 4. 1.1.2, enabling simultaneous analysis of measurement and structural models. The sample includes 800 engineers from diverse Moroccan industrial sectors, such as automotive, aerospace, IT, offshoring, and manufacturing. The chosen administration method for distributing the questionnaire was the online approach, widely preferred for its flexibility and ability to reach a geographically dispersed population. This method allowed for engaging a significant number of participants within a reasonable timeframe. The questionnaire link was shared through digital channels, including professional emails, professional social networks (LinkedIn), and specialized platforms within the targeted sectors.

2.2. Development of Hypothesis

Four hypotheses were developed to assess the impact of each KASH model dimension on engineers’ employability, aiming to identify the most predictive factors. The next section justifies each hypothesis:

Hypothesis 1 (H1).

Knowledge positively influences the employability of engineers.

Knowledge refers to the theoretical and technical understanding required to perform engineering tasks effectively. Engineers with up-to-date and relevant knowledge are better equipped to adapt to technological advancements and industry demand, thereby enhancing their employability [31].

Hypothesis 2 (H2).

Attitude positively influences the employability of engineers.

Attitude encompasses the mindset, motivation, and professional demeanor of engineers. A positive attitude, characterized by adaptability, resilience, and a willingness to learn, is essential for navigating the dynamic engineering landscape [31].

Hypothesis 3 (H3).

Skills positively influence the employability of engineers.

Skills refer to the practical abilities and competencies required to perform engineering tasks efficiently. In an era of rapid technological change, engineers must continuously develop both technical and soft skills, such as problem-solving, communication, and teamwork [32].

Hypothesis 4 (H4).

Habits positively influence the employability of engineers.

Habits represent the consistent behaviors and practices that engineers adopt in their professional lives. Effective habits, such as time management, continuous learning, and adherence to ethical standards, contribute to long-term career success [33].

Figure 1 below summarizes the hypotheses developed from our literature review.

2.3. Data Collection Process

The data collection process carried out for this empirical study was designed rigorously to ensure the relevance and reliability of the information gathered. The target population of this research consists of engineers working in various sectors, representing a diverse range of professional fields. This selection aims to provide a comprehensive and cross-sectional view of engineers’ behaviors and perceptions regarding the studied topics. Data was collected using a structured questionnaire specifically designed to meet the study’s objectives. The questionnaire was structured using the KASH model, incorporating questions that address Knowledge, Attitudes, Skills, and Habits to ensure a comprehensive analysis with clear, precise, and contextually relevant responses. The chosen administration method for distributing the questionnaire was the online approach, widely preferred for its flexibility and ability to reach a geographically dispersed population. This method allowed for engaging a significant number of participants within a reasonable timeframe. The questionnaire link was shared through digital channels, including professional emails, professional social networks (LinkedIn), and specialized platforms within the targeted sectors.

2.4. Sample Size Determination and Structural Equation Modeling Approach

The construction of our database focuses on determining the exact number of observations required. The sample size depends on several parameters, including the statistical methods used for data analysis. For our research, we opted for Structural Equation Modeling (SEM) using the SmartPLS 4. 1.1.2 (Partial Least Squares) [34].

This study used a survey among 800 engineers and a deductive analytic approach. An employability framework was used as a theoretical lens to explore engineers’ perceptions and experiences related to preparing for their careers. It was consolidated within the four dimensions of KASH model: “Knowledge”, “Attitude”, “Skills”, and “Habits”. Different industry sectors were targeted, including automotive, aeronautics, textile, electronics, it, offshoring, agriculture and others.

3. Results

3.1. Sample Distribution Based on Graduation Date and Time to First Employment

Figure 2a illustrates the distribution of engineers based on their graduation dates. The majority (84.25%) of degrees were earned recently, between 2017 and 2023, showing a significant rise in the number of graduates during this period. Engineers who graduated between 2011 and 2016 make up 6.25% of the sample, while those who graduated between 2000 and 2010 represent 5.88%. Graduates before 2000 are very few, and there are almost no graduates after 2023. These trends suggest a notable increase in engineering graduates, likely driven by rising demand for these skills or greater access to these educational programs.

Figure 2b shows the distribution of first hire dates for a sample of engineers. The majority (81%) of first hires occurred between 2017 and 2023, highlighting a significant recent influx of engineers into the job market. Engineers hired between 2011 and 2016 make up 9.5% of the sample, while those hired between 2000 and 2010 represent 5.88%. First hires before 2000 are rare (0.63%), and there is little data on hires after 2023. This pattern suggests a recent surge in engineering employment, likely driven by increased demand for technical skills or growth in specific industries.

3.2. The KASH Model and the Employability of Engineers in Service: Causal Effect and Empirical Validation

3.2.1. Reliability and Validation of the Measurement Model

In Structural Equation Modeling, item reliability is assessed through what are called “factor loadings”. These loadings reflect how strongly each measurement item (question or indicator) is related to the concept it is supposed to measure. According to widely accepted standards, an item is considered reliable if it explains at least 50% of the variance in its indicator. This corresponds to a loading value of 0.707 or higher, which is generally seen as the minimum acceptable threshold for good measurement quality [35]. We note that all the items evaluated display levels deemed very acceptable and satisfactory, demonstrating the overall coherence and relevance of the collected data. These results, which reflect remarkable quality on the different criteria examined, are presented in detail in Appendix A,

Table A1. Measurement Model Results—Item Reliability (Factor Loading).

Constructs	Items	Loading
ATTITUDE	ATTI1	0.903
	ATTI2	0.870
	ATTI3	0.877
	ATTI4	0.908
HABITS	HAB1	0.804
	HAB2	0.890
	HAB3	0.889
	HAB4	0.820
SKILLS	SKI1	0.915
	SKI2	0.865
	SKI3	0.857
	SKI4	0.911
KNOWLEDGE	KNOW1	0.844
	KNOW2	0.896
	KNOW3	0.876
	KNOW4	0.735
	KNOW5	0.849
EMPLOYABILITY	EMPL-M	1.000

. For the second criterion, namely the average variance extracted, we found that the values of all variables meet the threshold of 0.50. This level means that the variables or the construct explain more than 50% of the variance of their corresponding items (Appendix A,

Table A2. Average Variance Extracted (AVE).

Constructs	Average Variance Extracted (AVE)
ATTITUDE	0.792
HABITS	0.725
KNOWLEDGE	0.709
SKILLS	0.787

).

Concerning the last criterion used to assess convergent validity, namely composite reliability and Cronbach’s alpha, the values of all variables respect the threshold of 0.70 recommended. Also, in Appendix A,

Table A3. The composite reliability of the construct.

Constructs.	Cronbach’s Alpha	Composite Reliability
ATTITUDE	0.912	0.871
HABITS	0.874	0.975
KNOWLEDGE	0.897	0.957
SKILLS	0.910	0.849

, we note that the Cronbach’s Alpha coefficient presents very satisfactory levels. This provides an additional contribution to convergent reliability.

3.2.2. Testing the Structural Model Hypothesis

The validation of the hypotheses relies on the significance of the model’s structural relationships. Two key indicators were used: the T-statistic and the P-value. A relationship is considered significant when the T-value exceeds 1.96, corresponding to a 5% significance level. Similarly, a hypothesis is accepted only if the p-value is below 0.05, indicating statistical significance at the 95% confidence level. The results of the hypothesis testing are presented in

Table 1. Hypothesis Testing of the Structural Model.

Hypothesis	Relation	Std. Beta	Std. Error	T-Value	p-Value	Decision
H1	Attitude -> Employability	0.486	0.035	13.982	0.000	Confirmed **
H2	Habits -> Employability	0.132	0.036	3.629	0.000	Confirmed *
H3	Knowledge -> Employability	0.420	0.045	9.323	0.000	Confirmed **
H4	Skills -> Employability	0.181	0.036	4.967	0.000	Confirmed **

* Significant relationship. ** Strongly significant relationship.

below.

The results confirm the validity of Hypothesis H1, indicating that attitude is a key determinant of employability. Similarly, habits show a positive and significant effect on employability, supporting Hypothesis H2. Knowledge also demonstrates a strong and statistically significant impact (β = 0.420; T = 9.323; p = 0.000 < 0.05), validating Hypothesis H3. Finally, Hypothesis H4 is supported, as skills exhibit a positive and significant relationship with employability, highlighting the contribution of technical and practical competencies. The hypothesis testing results, summarized in Table 1, illustrate the structural relationships among the model’s latent variables.

Figure 3 illustrates the measurement model, highlighting the relationships between the latent constructs—attitude, habits, knowledge, and skills—and the dependent variable, employability. All outer loadings are statistically significant (p < 0.001), indicating strong associations between the observed indicators and their respective constructs. Additionally, all path coefficients from the independent variables to employability are significant (p = 0.000), confirming the robustness of the structural relationships. The R² value of 0.716 suggests that the model explains 71.6% of the variance in employability, indicating substantial explanatory power.

4. Discussion

This study offers valuable insights into the factors influencing engineers’ employability in the context of Industry 4.0, particularly within the Moroccan industrial sector. The findings reveal that attitude and knowledge have a strong impact on employability, while skills and habits exhibit moderate effects. These results are consistent with prior research underscoring the combined importance of technical expertise and behavioral competencies in adapting to the evolving demands of the Industry 4.0 job market.

4.1. Attitude and Knowledge

The findings indicate that both attitude and knowledge significantly influence engineers’ employability, with attitude exerting the strongest effect (β = 0.486, p < 0.001). This underscores the value of mindset-related attributes such as adaptability, teamwork, and lifelong learning. Knowledge also has a substantial impact (β = 0.420, p < 0.001). These results highlight the need for engineering education to balance personal development with technological expertise to better align graduates with evolving labor market demands.

4.2. Skills and Habits

Skills and habits positively impact employability, but their effects are weaker (β = 0.181 and β = 0.132, respectively) compared to attitude and knowledge. While technical and soft skills, along with professional habits like time management and ethics, are important, they are less decisive in employability. Nonetheless, skills and habits remain crucial for long-term career success. Future research could investigate their interaction with attitude and knowledge to provide a more holistic view of employability.

4.3. Implications for Engineering Schools

The results of this study have significant implications for engineering education in Morocco. To enhance employability, engineering schools should adopt a more holistic approach that integrates technical and technological knowledge (AI, IoT, etc.) with training in soft skills and generic competencies. This could include project-based learning, interdisciplinary collaboration, and exposure to real-world industry challenges. Additionally, fostering a culture of continuous learning and adaptability will be crucial in preparing engineers for the uncertainties of Industry 4.0.

4.4. Limitations and Future Research

While this study provides valuable insights, it is not without limitations. The sample, although representative of Moroccan engineers, may not fully capture the diversity of global engineering labor markets. Future research could expand the scope to include engineers from different regions and industries to validate the generalizability of the findings. Additionally, longitudinal studies could provide deeper insights into how employability factors evolve over time in response to technological advancements and market demands. To better align engineering education with labor market demands, further investigation should focus on employer perspectives. Conducting interviews with HR managers, technical recruiters, and company executives from diverse industries would provide valuable insights into current hiring trends and skill gaps.